
All models are more robust to Gaussian noise than adversarial attacks, but our 01 loss network augmented with Gaussian noise during training has a higher accuracy on large distortions

The minimum distortion to make an image adversarial is higher for our 01 loss network compared to the standard sigmoid activated cross-entropy loss network and binarized networks

Substitute model black box attacks are far less effective on our 01 loss network compared to the standard sigmoid activated cross-entropy loss network and binarized networks

Compared to simple convolutional neural networks like LeNet [1]} our model (without convolutions) has higher accuracies on adversarial examples from substitute model black box attacks when the distortions are high.

